Exponential View report finds generative AI reached $110 billion in revenue over the past year and a $175 billion run rate · Digg

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Exponential View report finds generative AI reached $110 billion in revenue over the past year and a $175 billion run rate

Story Overview

A fresh bottom-up analysis from Exponential View tallies genuine end-customer spending on generative AI at $110 billion for the trailing year, pushing the annualized run rate past $175 billion. The report stresses deduplication to avoid inflating figures through the supply chain and notes this pace outstrips the mobile era by a factor of three.

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Original post

Azeem Azhar@azeem

The GenAI economy has generated $110 billion in sales over the past 12 months. It is growing fast. On an annualized basis, the revenue run rate exceeds $175 billion.

These numbers took us several months to construct, and as far as we know, it’s the first bottom-up, deduplicated measure of consumer and enterprise AI spending across the full stack.

We are releasing this research today in our first The State of the AI Economy report.

https://intelligence.exponentialview.co/

4:56 AM · Jun 25, 2026 · 975.6K Views

Cost Pressure

Infrastructure costs are already biting

Even at these levels, the spend only just matches depreciation on the specialized hardware, leaving little margin once power and other expenses enter the picture.

Open Question

Price cuts could unlock bigger volumes

The authors model how further token price cuts could drive even higher consumption while total spend keeps climbing, though four different demand scenarios remain on the table depending on capability gains.

Sentiment

Positive users praise the GenAI revenue report for its rich data and analysis, while negative users question the figures and blame AI for job losses and economic harm.

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Neg

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31 comments with sentiment.

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The State of the AI Economy — Exponential View

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VIEWS26.7KLIKES167

Azeem Azhar@azeem

4/ AI has revived a flatlined US power sector: electricity generation was roughly zero-growth from 2008-2024, now +9 TWh/month.

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BOOKMARKS50

Azeem Azhar@azeem

13/ Our analysis suggests that AI demand is more revenue-validated than any prior platform shift. The investment case comes down to whether falling prices can move enough token volume to earn a return on CapEx.

FULL REPORT: https://www.exponentialview.co/p/the-state-of-the-ai-economy

Thanks to @alexolegimas @jaimesevillamol @shanumatthew93 for early feedback on the report.

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RETWEETS100

Rohan Paul@rohanpaul_ai

This is a brilliant report. The State of the AI Economy by @exponentialview

- $110B real AI revenue over 12 months, after removing double-counting. so $1 spent on Claude is counted once, even if part of it later flows to Amazon or another infrastructure provider.

- $175B current annualized run rate, showing fast acceleration. Measured by end-customer spend, not supply-chain pass-through revenue. Excludes China, internal AI savings, ad uplift, consulting, and systems integration.

- Growth running roughly 3x faster than mobile or internet adoption waves.

- The pace of revenue formation has sharply accelerated. New $1B revenue now arrives in under 2 days, versus 180 days in 2023.

- Enterprise AI has moved beyond pilots, but deep company-wide rollout is still early.

- AI earnings-call mentions reached 31% of tracked S&P 500 firms.

- Only 20% of tracked firms made quantified AI impact claims.

- Hyperscaler AI revenue roughly covers AI infrastructure depreciation for now. GPU economics depend heavily on 6-year compute life assumptions. Other AI infrastructure gets modeled over 14 years.

- Token price cuts do not automatically reduce revenue.

- Every 10% token price cut drives 12-18% more token usage.

- AI demand looks price elastic, meaning cheaper AI expands usage faster than prices fall.

- Power availability and data-center costs remain major limits on future scaling.

Azeem Azhar@azeem

The GenAI economy has generated $110 billion in sales over the past 12 months. It is growing fast. On an annualized basis, the revenue run rate exceeds $175 billion.

These numbers took us several months to construct, and as far as we know, it’s the first bottom-up, deduplicated measure of consumer and enterprise AI spending across the full stack.

We are releasing this research today in our first The State of the AI Economy report.

https://intelligence.exponentialview.co/

1d70.6K468428

REPLIES7

Turner Novak 🍌🧢@TurnerNovak

"In 2023, it took 180 days to add $1bn of cumulative AI revenue; it now takes under TWO days. This is 90x faster."

Azeem Azhar@azeem

The GenAI economy has generated $110 billion in sales over the past 12 months. It is growing fast. On an annualized basis, the revenue run rate exceeds $175 billion.

These numbers took us several months to construct, and as far as we know, it’s the first bottom-up, deduplicated measure of consumer and enterprise AI spending across the full stack.

We are releasing this research today in our first The State of the AI Economy report.

https://intelligence.exponentialview.co/

3h2.3K80

Azeem Azhar@azeem

2/ We estimate that AI is scaling 3x faster than the previous IT waves. This growth has held across all adoption phases since 2022, from chatbots to copilots to agentic AI.

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Azeem Azhar@azeem

7/ Demand is so tight that even old, fully-depreciated GPUs still earn scarcity rents.

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Azeem Azhar@azeem

3/ In 2023, it took 180 days to add $1bn of cumulative revenue; it now takes under TWO days. This is 90x faster.

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Azeem Azhar@azeem

6/ $2 trillion of CapEx is committed through 2026. This is the largest buildout in tech history and is increasingly funded by external debt rather than cash. As of Q4 2025, AI revenue only just covers the depreciation on that buildout and that's before every other cost.

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Azeem Azhar@azeem

5/ Compared with the wider economy, AI revenues remain tiny. It’s worth noting that all previous general-purpose technologies behaved similarly.

Electricity made light ~99.97% cheaper, but this gain wasn’t recorded in the GDP. When evaluated against the value consumers place on AI, we estimate that it tops monthly genAI revenues by 30%.

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Azeem Azhar@azeem

@alexolegimas @ShanuMathew93 @Jsevillamol Also covered in Bloomberg: https://www.bloomberg.com/news/articles/2026-06-25/ai-demand-begins-to-justify-massive-cost-of-data-center-buildout?srnd=undefined

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Azeem Azhar@azeem

10/ No one has yet cracked how to price the “intelligence” a token delivers.

Tokens are the billing metric of the AI economy, but not yet the unit of value we need to estimate the useful output of AI use.

We suggest that quality-adjusted tokens come closest to the unit of value we need. Over the past year, quality-adjusted tokens have kept pace with raw token growth.

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Azeem Azhar@azeem

12/ Revenue is concentrated in infrastructure today, but value is visibly moving toward apps and models.

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Azeem Azhar@azeem

11/ The frontier is a treadmill. Last year’s best models commoditize into open weights within a year, and the most sophisticated users are already leaving.

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Azeem Azhar@azeem

9/ The efficiency gains drive lower token prices, but this is offset by higher demand. Industry-wide revenue per GW of data center capacity passed $7bn/GW.

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Ed Zitron@edzitron

@azeem Hey Azeem can you please share the data as far as how you attributed revenue beyond the deduping?

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Azeem Azhar@azeem

8/ Token volumes top 30 trillion a month, growing 14x YoY.

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Rohan Paul@rohanpaul_ai

AI revenue has crossed its first serious accounting test: $25B in quarterly sales now exceeds $21B in estimated chip and data-center depreciation.

i.e. AI infrastructure is starting to pay for itself before power, labor, financing, and leases are counted.

---

Chart from Bloomberg

bloomberg. com/news/articles/2026-06-25/ai-demand-begins-to-justify-massive-cost-of-data-center-buildout

Rohan Paul@rohanpaul_ai

This is a brilliant report. The State of the AI Economy by @exponentialview

- $110B real AI revenue over 12 months, after removing double-counting. so $1 spent on Claude is counted once, even if part of it later flows to Amazon or another infrastructure provider.

- $175B current annualized run rate, showing fast acceleration. Measured by end-customer spend, not supply-chain pass-through revenue. Excludes China, internal AI savings, ad uplift, consulting, and systems integration.

- Growth running roughly 3x faster than mobile or internet adoption waves.

- The pace of revenue formation has sharply accelerated. New $1B revenue now arrives in under 2 days, versus 180 days in 2023.

- Enterprise AI has moved beyond pilots, but deep company-wide rollout is still early.

- AI earnings-call mentions reached 31% of tracked S&P 500 firms.

- Only 20% of tracked firms made quantified AI impact claims.

- Hyperscaler AI revenue roughly covers AI infrastructure depreciation for now. GPU economics depend heavily on 6-year compute life assumptions. Other AI infrastructure gets modeled over 14 years.

- Token price cuts do not automatically reduce revenue.

- Every 10% token price cut drives 12-18% more token usage.

- AI demand looks price elastic, meaning cheaper AI expands usage faster than prices fall.

- Power availability and data-center costs remain major limits on future scaling.

2h2.9K166

Azeem Azhar@azeem

Hey @edzitron, I saw quite a few questions around so I'm compiling some answers here. We have built individual, proprietary financial models for the largest players (OAI, Anth, Google, MSFT and many others), and slimmer models for others in the market, based on filed financials, statements & leaks (such as your recent OpenAI work) and other signals.

We triangulate/cross-check against top-down industry numbers, analyst estimates, counterparty revenues, and fundamental constraints (e.g. the volume you can physically run on a given amount of compute). From this, we have the raw (undeduped) revenues. NB this does not pretend that financing, investment or CapEx are revenue.

We then look into the gross margins (token cost at the app layer, and inference hosting at the foundation model layer) to remove revenue that is double/triple-counted. You’ll see on slide 55 that the Foundation Model layer is much smaller than the reported top-line revenues of OpenAI or Anthropic, because their inference costs are revenue for the hosting layer. And if more pressure on pricing (as covered in slides 56-62) pushes foundation model gross margins thinner, we could see that layer of revenue shrink as a portion of the whole.

To your other point, for our comparison of revenues vs capex depreciation, we primarily compare only the revenues of the hyperscalers and neoclouds to their AI CapEx on slides 32-34 (which is where spending on chips is counted as a capital cost), and separately compare the whole market (deduplicated) revenues, to show the portion of revenue available to cover all their other costs – currently 81% of all hyperscaler/neocloud revenue (and 68% of all revenue) is eaten up by this CapEx charge on an ongoing basis, which highlights the fine balance!

More on methodology, beyond what is in the report, coming soon. Hope this is useful.

20h92584

Azeem Azhar@azeem

@alexolegimas Thanks to @alexolegimas @ShanuMathew93 @Jsevillamol

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John O'Connell@johnoconnell836

@azeem Can't wait until it crashes and the scourge of AI is wiped off the face of the earth

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